Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch
Quick Take
Parallax introduces a learned projector to replace LLA's per-query solver, achieving double the arithmetic intensity and enhancing perplexity at 0.6B and 1.7B parameters. This advancement significantly impacts model efficiency and performance in local linear attention mechanisms.
Key Points
- Parallax replaces LLA's per-query solver with a learned projector.
- Achieves double the arithmetic intensity compared to previous models.
- Improves perplexity metrics at 0.6B and 1.7B parameters.
- Enhances local linear attention mechanisms significantly.
- Targets improvements in model efficiency and performance.
Article Excerpt
From source RSS / original summaryParallax replaces LLA's per-query solver with a learned projector, doubling arithmetic intensity and improving perplexity at 0. 6B and 1. 7B. The post Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch appeared first on MarkTechPost.
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